Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach

• Published in: Remote sensing of environment Vol. 238; p. 111356
• Main Authors: Brown, Jesslyn F., Tollerud, Heather J., Barber, Christopher P., Zhou, Qiang, Dwyer, John L., Vogelmann, James E., Loveland, Thomas R., Woodcock, Curtis E., Stehman, Stephen V., Zhu, Zhe, Pengra, Bruce W., Smith, Kelcy, Horton, Josephine A., Xian, George, Auch, Roger F., Sohl, Terry L., Sayler, Kristi L., Gallant, Alisa L., Zelenak, Daniel, Reker, Ryan R., Rover, Jennifer
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.03.2020; Elsevier BV
• Subjects: Algorithms; Analysis Ready Data; Change detection; Classification; data collection; decision support systems; Decision trees; Earth observations; Environment models; Environmental modeling; environmental models; Geological surveys; Information management; Land cover; Land management; Landsat; Machine learning; managers; Monitoring; prototypes; remote sensing; statistics; Temporal variability; temporal variation; Time series; United States; United States Geological Survey; United States > US; United States; Change detection; Time series; Landsat; Earth observations; Land cover; Analysis Ready Data; Monitoring
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2019.111356

Growing demands for temporally specific information on land surface change are fueling a new generation of maps and statistics that can contribute to understanding geographic and temporal patterns of change across large regions, provide input into a wide range of environmental modeling studies, clarify the drivers of change, and provide more timely information for land managers. To meet these needs, the U.S. Geological Survey has implemented a capability to monitor land surface change called the Land Change Monitoring, Assessment, and Projection (LCMAP) initiative. This paper describes the methodological foundations and lessons learned during development and testing of the LCMAP approach. Testing and evaluation of a suite of 10 annual land cover and land surface change data sets over six diverse study areas across the United States revealed good agreement with other published maps (overall agreement ranged from 73% to 87%) as well as several challenges that needed to be addressed to meet the goals of robust, repeatable, and geographically consistent monitoring results from the Continuous Change Detection and Classification (CCDC) algorithm. First, the high spatial and temporal variability of observational frequency led to differences in the number of changes identified, so CCDC was modified such that change detection is dependent on observational frequency. Second, the CCDC classification methodology was modified to improve its ability to characterize gradual land surface changes. Third, modifications were made to the classification element of CCDC to improve the representativeness of training data, which necessitated replacing the random forest algorithm with a boosted decision tree. Following these modifications, assessment of prototype Version 1 LCMAP results showed improvements in overall agreement (ranging from 85% to 90%). •We developed a robust capability for operational monitoring of land surface change.•Landsat ARD and Continuous Change Detection and Classification are foundational.•Landsat's rich time series has substantial variability in observation frequency.•The algorithm was modified reducing variability in results between scene centers and overlap zones.•Classification was modified to improve training data representativeness and reduce artifacts.